THE field of lipidomics offers significant potential for advancing precision medicine, but its progress has been hindered by a fundamental challenge: a lack of data consistency between laboratories. As the number of clinical fields using lipid analysis grows, the ability to generate reliable, quantitative, and therefore comparable lipidomics data has become essential.
However, differences in analytical methods from lab to lab create data that is difficult to compare. This inconsistency has been called a “substantial roadblock” for using lipidomics in a clinical setting, as the lack of comparable quantification complicates the validation of research findings across different studies. Also, the use of AI methods to study the lipidomes of batches of samples and comparing their lipidomes demands reproducible quantification to be successful.
To quantify the extent of this issue, the National Institute of Standards and Technology (NIST) organized a comprehensive interlaboratory study. The study was designed to capture a realistic snapshot of measurement practices across the lipidomics community.
Thirty-one national and international laboratories participated, including academic centers, government agencies, and commercial companies like Lipotype. The basis for the study was a commercially available human plasma material known as Standard Reference Material (SRM) 1950. It was created from a pool of plasma from 100 fasted individuals between 40 and 50 years old. These donors were selected to represent the general US population according to race, sex, and health, and individuals with extreme health conditions were excluded, making the material a suitable reference for the comparison study.
Each laboratory was instructed to:
– Analyze the sample using their own established procedures. No specific methods were mandated for sample preparation or analysis, which was a key decision to ensure the study measured real-world variability.
– Report the lipids they identified and their concentrations. Labs submitted data from three replicate analyses.
To compare the submitted data, all lipid names were converted to a common nomenclature. The final “consensus values” for each lipid were then calculated using a statistical method (the median of laboratory means) that is robust against extreme outliers, providing a reliable central value from the diverse dataset.
The findings provided a clear, quantitative look at the state of lipid analysis. A total of 1,527 unique lipids were reported across all labs. Strikingly, 745 of these, nearly half, were reported by only a single laboratory, indicating significant differences in analytical capabilities and reporting. The reported concentrations for the same lipids often varied widely. Key lipid classes such as ceramides (Cer), diacylglycerols (DAG), and triacylglycerols (TAG) showed particularly high variability, with coefficients of dispersion often exceeding 40%. The study identified the choice of standards as a major source of quantitative differences. The type of standard used by a lab was found to have a large influence on the final reported concentrations.
Absolute quantification in lipidomics – determining the precise concentration of a lipid in a sample – relies on internal standards: reference molecules of known concentration added to the sample at the start of extraction. These standards serve a dual purpose: they monitor whether the extraction and analytical procedures are performed as expected, and they provide the reference point for concentration calculations. The ratio between the amount added and the amount detected at the end is used to calculate the true concentration of every other lipid in the sample, making results accurate and comparable across samples and laboratories.
This study found that this process is only reliable when the same correct internal standards are used. A significant obstacle to consistent quantitation within the community is the absence of suitable internal standards. The specific internal standards employed had a substantial impact on the reported final lipid concentration. For example, if a laboratory quantified a lipid class with an internal standard from a different class, the concentration values often differed considerably from those obtained by laboratories using standards from the appropriate lipid class. This finding underscores that the best practice for achieving absolute lipid quantification is to use at least one dedicated, class-specific internal standard for each lipid class being measured, with additional standards required in specific cases.
Despite these findings, the study provided a constructive path forward. By analyzing the collective data, the organizers established consensus reference values for 339 lipids in SRM 1950. These values now serve as a public benchmark, giving the entire community a tool to assess their methods, validate results, and perform quality control.
Lipid class composition of NIST SRM 1950 human plasma, shown by (A) the number of detected and identified lipid species and (B) by concentration. Only lipid species quantified by at least five participating laboratories are included (n = 339).
Bowden et al., JLR 2017; 58, 2275-2288. 10.1194/jlr.M079012
Lipotype’s participation in this 2017 study reflects a long-term commitment to improving measurement quality. Over a decade, Lipotype has built a comprehensive quality framework to ensure reproducibility and cross-laboratory comparability of lipidomics results. This includes lipid class-specific internal standards, sometimes multiple per class, as well as commercial quantitative internal standard mixtures, and reference samples run alongside every analysis type. Methods validation in Lipotype is performed according to the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), which is the highest standard in the pharmaceutical industry. System suitability is monitored through LipidQC prior to each experiment, and all workflows are governed by validated software and standardized SOPs. As an active member of the lipidomics community, Lipotype participates in ring trials, standardization initiatives, and interlaboratory studies, contributing to and advocating for reliable absolute quantification standards across the field.
The 2017 NIST study was a pivotal moment for the lipidomics field. It demonstrated that for lipidomics to be applied effectively in a clinical context, the community must move toward standardized methods that prioritize absolute quantification. The reference values established by the study are a critical tool for building a foundation of reliable and comparable data. Lipotype’s involvement in this work highlights our dedication to this principle. By championing standardization and providing high-quality, absolutely quantified lipid data, Lipotype continues to support the scientific and medical communities in translating lipid research into meaningful clinical applications.
Lipotype Lipidomics technology delivers absolute quantification of thousands of lipid species across all major lipid classes, using robust mass spectrometry platforms built for reproducibility at scale. From research in cardiovascular diseases to biomarker discovery, Lipotype provides the data quality, coverage, and throughput needed to make lipidomics a reliable part of your study design.
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